Cooperative Formation Control Strategy for Multi-robot System Based on APF Algorithm and Sliding Mode Estimator

Abstract

In this paper, a cooperative formation control strategy for the multi-robot system based on sliding mode estimator and Artificial Potential Field (APF) algorithm is proposed to solve the problem of formation maintenance, trajectory tracking and collision avoidance in a multi-obstacle environment. The desired information of the formation robots is given by a virtual leader, and the state information of the virtual leader can only be obtained by some of the follower robots. Firstly, based on the consensus algorithm, a finite time distributed sliding mode estimator is designed for each follower robots to obtain the desired information. Secondly, based on the error information of the follower robots, a distributed Model Predictive Control (MPC) formation controller is designed to address the issues of forming and maintaining a desired formation while tracking an desired trajectory. Then, an obstacle avoidance function of the multi-robot formation for obstacle avoidance and collision avoidance is constructed based on the APF algorithm, and a Lyapunov function is designed for stability analysis. The estimator and controller are combined to form a formation cooperative control strategy, which together ensure the formation structural performance and safety stability of the multi-robot system. Finally, simulation experiments are conducted to verify the effectiveness and robustness of the proposed cooperative control strategy by combining typical roads and actual obstacle scenarios in an intelligent factory environment.